Abstract: A graphical password authentication method is based on sketches drawn by user. The method extracts a template edge orientation pattern from an initial sketch of the user and an input edge orientation pattern from an input sketch of the user, compares the similarity between the two edge orientation patterns, and makes an authentication decision based on the similarity. The edge orientations are quantized, and each edge orientation pattern includes a set of quantized orientation patterns each corresponding to one of the quantized edge orientations. The number of quantized edge orientations, as well as other parameters such as the dimension of the final orientation patterns and acceptance threshold, can be optimized either globally or user-specifically.

Abstract: The present invention aims at providing a sequential data examination method which can increase data examination accuracy compared with the prior art. The similarity is calculated between a layered network model generated from learning sequential data to be learned and a layered network model generated from testing sequential data to be tested. Based on the similarity, it is determined whether or not the testing sequential data to be tested belong to one or more categories. A network model for each layer of the layered network model is constructed by multiplying an element of the feature vector and its corresponding Eigen co-occurrence matrix.

Abstract: A graphical password authentication method is based on sketches drawn by user. The method extracts a template edge orientation pattern from an initial sketch of the user and an input edge orientation pattern from an input sketch of the user, compares the similarity between the two edge orientation patterns, and makes an authentication decision based on the similarity. The edge orientations are quantized, and each edge orientation pattern includes a set of quantized orientation patterns each corresponding to one of the quantized edge orientations. The number of quantized edge orientations, as well as other parameters such as the dimension of the final orientation patterns and acceptance threshold, can be optimized either globally or user-specifically.

Abstract: The present invention aims at providing a sequential data examination method which can increase data examination accuracy compared with the prior art. The similarity is calculated between a layered network model generated from learning sequential data to be learned and a layered network model generated from testing sequential data to be tested. Based on the similarity, it is determined whether or not the testing sequential data to be tested belong to one or more categories. A network model for each layer of the layered network model is constructed by multiplying an element of the feature vector and its corresponding Eigen co-occurrence matrix.